If the training corpus for a machine learning model contains stereotypes and biases, then the output of the model will reflect those prejudices.
When that model is used in large-scale applications, it will not just repeat those biases, it will amplify them. It propagates biased language, which feeds back into the model in a self-reinforcing vicious cycle of increasing bias.
Should we attempt to detect prejudices in machine learning models and actively counter them, creating a self-reinforcing virtuous cycle of balance and tolerance?
----
As an aside, for an article about language, this one has a lot of typos. Doesn't anyone proofread before posting?
If the training corpus for a machine learning model contains stereotypes and biases, then the output of the model will reflect those prejudices.
When that model is used in large-scale applications, it will not just repeat those biases, it will amplify them. It propagates biased language, which feeds back into the model in a self-reinforcing vicious cycle of increasing bias.
Should we attempt to detect prejudices in machine learning models and actively counter them, creating a self-reinforcing virtuous cycle of balance and tolerance?
----
As an aside, for an article about language, this one has a lot of typos. Doesn't anyone proofread before posting?